Search (78 results, page 2 of 4)

  • × theme_ss:"Retrievalalgorithmen"
  1. Al-Hawamdeh, S.; Smith, G.; Willett, P.; Vere, R. de: Using nearest-neighbour searching techniques to access full-text documents (1991) 0.01 
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  2. Jones, G.; Robertson, A.M.; Willett, P.: ¬An introduction to genetic algorithms and to their use in information retrieval (1994) 0.01 
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  3. Srinivasan, P.: Query expansion and MEDLINE (1996) 0.01 
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  4. Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995) 0.01 
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  5. Robertson, M.; Willett, P.: ¬An upperbound to the performance of ranked output searching : optimal weighting of query terms using a genetic algorithms (1996) 0.01 
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  6. Chakrabarti, S.; Dom, B.; Kumar, S.R.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Kleinberg, J.M.; Gibson, D.: Neue Pfade durch den Internet-Dschungel : Die zweite Generation von Web-Suchmaschinen (1999) 0.01 
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  7. Thompson, P.: Looking back: on relevance, probabilistic indexing and information retrieval (2008) 0.01 
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  8. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22
  9. Faloutsos, C.: Signature files (1992) 0.01 
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    Date
    7. 5.1999 15:22:48
  10. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01 
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    Date
    22. 3.2003 19:27:23
  11. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18
  12. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01 
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    Date
    13. 9.2014 14:45:22
  13. Urbain, J.; Goharian, N.; Frieder, O.: Probabilistic passage models for semantic search of genomics literature (2008) 0.01 
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    Abstract
    We explore unsupervised learning techniques for extracting semantic information about biomedical concepts and topics, and introduce a passage retrieval model for using these semantics in context to improve genomics literature search. Our contributions include a new passage retrieval model based on an undirected graphical model (Markov Random Fields), and new methods for modeling passage-concepts, document-topics, and passage-terms as potential functions within the model. Each potential function includes distributional evidence to disambiguate topics, concepts, and terms in context. The joint distribution across potential functions in the graph represents the probability of a passage being relevant to a biologist's information need. Relevance ranking within each potential function simplifies normalization across potential functions and eliminates the need for tuning of passage retrieval model parameters. Our dimensional indexing model facilitates efficient aggregation of topic, concept, and term distributions. The proposed passage-retrieval model improves search results in the presence of varying levels of semantic evidence, outperforming models of query terms, concepts, or document topics alone. Our results exceed the state-of-the-art for automatic document retrieval by 14.46% (0.3554 vs. 0.3105) and passage retrieval by 15.57% (0.1128 vs. 0.0976) as assessed by the TREC 2007 Genomics Track, and automatic document retrieval by 18.56% (0.3424 vs. 0.2888) as assessed by the TREC 2005 Genomics Track. Automatic document retrieval results for TREC 2007 and TREC 2005 are statistically significant at the 95% confidence level (p = .0359 and .0253, respectively). Passage retrieval is significant at the 90% confidence level (p = 0.0893).
  14. Jacucci, G.; Barral, O.; Daee, P.; Wenzel, M.; Serim, B.; Ruotsalo, T.; Pluchino, P.; Freeman, J.; Gamberini, L.; Kaski, S.; Blankertz, B.: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval (2019) 0.01 
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  15. Srinivasan, P.: Intelligent information retrieval using rough set approximations (1989) 0.01 
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  16. Robertson, S.E.: OKAPI at TREC-3 (1995) 0.01 
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    Type
    p
  17. Bhogal, J.; Macfarlane, A.; Smith, P.: ¬A review of ontology based query expansion (2007) 0.01 
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  18. Ning, X.; Jin, H.; Jia, W.; Yuan, P.: Practical and effective IR-style keyword search over semantic web (2009) 0.01 
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  19. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.01 
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    Date
    1. 8.1996 22:08:06
  20. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01 
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    Date
    20. 3.2005 16:23:22

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